Biology subjects

Neumann, A. U.

Publications and source records attributed to Neumann, A. U..

3 recordsLinked to original sources

bioRxiv2024

AnnotIEM: novel tool for microbiome species-level annotation of 16S gene based microbial sequencing

Identification of microbial species is essential for the generation of meaningful results from microbiome analysis. However, correct annotation of 16S microbial sequences remains a challenge, especially down to the species-level, because most available databases of 16S sequences are not well curated. AnnotIEM is a new tool for species-level annotation of sequences derived from 16S rRNA gene sequencing. AnnotIEMs novel hit-selection algorithm combines top-hit and majority-hit alignment results from multiple databases. Benchmarking using in-vitro and in-silico mock community control datasets showed that AnnotIEMs annotation precision on species-level (median=0.83) is significantly (p<0.0001) better than any currently available annotation tool. Benchmarking using a number of real-world environmental microbiome studies demonstrated that AnnotIEM gives higher fraction (58-68%) of species-level annotated sequences, as compared to any other currently available tool. In conclusion, AnnotIEM significantly improves bacterial annotation, which can greatly enhance the results of any microbiome study, and could be potentially used also for other organisms.

bioinformatics↗

bioRxiv2023

Bioinformatic correction of bacterial morphology-based extraction bias and chimeras in microbiome sequencing data

IntroductionMicrobiome amplicon sequencing data are distorted by multiple protocol-dependent biases, originating from bacterial DNA extraction, contamination, sequence errors, and chimeras. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to bioinformatically correct extraction bias based on morphological properties. MethodsWe compared dilution series of 3 mock communities with an even or staggered composition. DNA was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1-V3 16S rRNA gene) together with corresponding DNA mocks. Sequences were denoised using DADA2, and annotated by matching against mock reference genomes. ResultsMicrobiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Independent of the extraction protocol, chimera formation increased with high input cell number. Contaminants originated mostly from buffers, and considerable cross-contamination was observed in low-input samples. Comparison of microbiome composition of the cell mocks to corresponding DNA mocks revealed taxon-specific protocol-dependent extraction bias. Strikingly, this extraction bias per species was predictable by bacterial cell morphology. Morphology-based bioinformatic correction of extraction bias significantly improved sample compositions when applied to different samples, even with different taxa. ConclusionsOur results indicate that higher DNA density increases chimera formation during PCR amplification. Furthermore, we show that bioinformatic correction of extraction bias is feasible based on bacterial cell morphology.

microbiology↗

bioRxiv2020

Distribution of ACE2, CD147, cyclophilins, CD26 and other SARS-CoV-2 associated molecules in human tissues and immune cells in health and disease

BackgroundMorbidity and mortality from COVID-19 caused by novel coronavirus SARS-CoV-2 is accelerating worldwide and novel clinical presentations of COVID-19 are often reported. The range of human cells and tissues targeted by SARS-CoV-2, its potential receptors and associated regulating factors are still largely unknown. The aim of our study was to analyze the expression of known and potential SARS-CoV-2 receptors and related molecules in the extensive collection of primary human cells and tissues from healthy subjects of different age and from patients with risk factors and known comorbidities of COVID-19. MethodsWe performed RNA sequencing and explored available RNA-Seq databases to study gene expression and co-expression of ACE2, CD147 (BSG), CD26 (DPP4) and their direct and indirect molecular partners in primary human bronchial epithelial cells, bronchial and skin biopsies, bronchoalveolar lavage fluid, whole blood, peripheral blood mononuclear cells (PBMCs), monocytes, neutrophils, DCs, NK cells, ILC1, ILC2, ILC3, CD4+ and CD8+ T cells, B cells and plasmablasts. We analyzed the material from healthy children and adults, and from adults in relation to their disease or COVID-19 risk factor status. ResultsACE2 and TMPRSS2 were coexpressed at the epithelial sites of the lung and skin, whereas CD147 (BSG), cyclophilins (PPIA and PPIB), CD26 (DPP4) and related molecules were expressed in both, epithelium and in immune cells. We also observed a distinct age-related expression profile of these genes in the PBMCs and T cells from healthy children and adults. Asthma, COPD, hypertension, smoking, obesity, and male gender status generally led to the higher expression of ACE2- and CD147-related genes in the bronchial biopsy, BAL or blood. Additionally, CD147-related genes correlated positively with age and BMI. Interestingly, we also observed higher expression of ACE2- and CD147-related genes in the lesional skin of patients with atopic dermatitis. ConclusionsOur data suggest different receptor repertoire potentially involved in the SARS-CoV-2 infection at the epithelial barriers and in the immune cells. Altered expression of these receptors related with age, gender, obesity and smoking, as well as with the disease status might contribute to COVID-19 morbidity and severity patterns.

immunology↗